Search Results (12,390)

The rapid deployment of photovoltaic generation increases uncertainty in power-system operation and strengthens the need for ultra-short-term forecasts with reliable uncertainty estimates. Point-forecasting approaches alone are often insufficient for dispatch and reserve decisions because they do not quantify risk. This study investigates probabilistic forecasting of short-horizon solar generation using quantile regression on a public dataset of solar output and meteorological variables. This study proposes a hybrid attention–convolution model that combines an attention-based encoder to capture long-range temporal dependencies with a causal temporal convolution module that extracts fast local fluctuations using only past information, preventing information leakage. The two representations are fused and decoded jointly across multiple future horizons to produce consistent quantile trajectories. Experiments against representative machine-learning and deep-learning baselines show improved probabilistic accuracy and competitive central forecasts, while illustrating an important sharpness–calibration trade-off relevant to risk-aware grid operation. Key novelties include a multi-horizon quantile formulation at 15 min resolution for one-hour-ahead PV increments, a HybridCNN–STTransformer that fuses causal temporal convolutions with Transformer attention, and a horizon-token decoder that models inter-horizon dependencies to produce consistent multi-step quantile trajectories; reliability/sharpness diagnostics and post hoc calibration are discussed for operational risk-aware use. Full article

This review deals with the issue of operational coal quality control using instrumental nuclear–physical methods. The existing traditional method of coal testing, characterized by high labor intensity and low representativeness, cannot serve as a basis for operational management of mining and processing processes. Instrumental nuclear–physical methods are free from these drawbacks; they are based on various processes of interaction of gamma and neutron radiation with substances. The main modifications of instrumental methods using gamma radiation are discussed: backscattering, forward gamma scattering, gamma absorption, gamma annihilation, and natural gamma activity. Various modifications of gamma methods are related to the energy of the primary and recorded radiation, the prevalence of a particular interaction process, the depth of the method, characteristics of the test object, the measurement geometry, and the other factors. The features of gamma methods are described in the context of the tasks being solved, interfering factors (variations in the bulk density, the moisture content, and the elemental composition), and methodological approaches for increasing the sensitivity and accuracy of the coal quality assessment. The variety of modifications of neutron methods is associated with irradiation of the analyzed coal with neutrons of different energies and detection of secondary gamma radiation arising from neutron activation of elements, inelastic scattering of fast neutrons, and radiative capture of thermal neutrons by the elements composing the coal. The methodological features of neutron activation, the neutron–gamma method of inelastic scattering and radiative capture are considered in the context of elemental analysis for Al, Si, S, Ca, Fe, H, C, and O and determining the ash content of coal in general. The main trends of the instrumental quality control are highlighted and recommendations are given for their use depending on the metrological characteristics and physical and chemical properties of the control object. The gamma-albedo method with registration of X-ray fluorescence of heavy gold-forming elements is the most promising for express analysis of powder samples. To test coarse coal in large amounts, multiparameter neutron methods are needed that comprehensively utilize high-precision equipment and instrumental signals from carbon, oxygen, and major ash-forming elements. Full article

Artificial intelligence transforms lubricant research by linking molecular modeling, diagnostics, and industrial operations into predictive systems. In this regard, machine learning methods such as Bayesian optimization and neural-based Quantitative Structure–Property/Tribological Relationship (QSPR/QSTR) modeling help to accelerate additive design and formulation development. Moreover, deep learning and hybrid physics–AI frameworks are now capable to predict key lubricant properties such as viscosity, oxidation stability, and wear resistance directly from molecular or spectral data, reducing the need for long-duration field trials like fleet or engine endurance tests. With respect to condition monitoring, convolutional neural networks automate wear debris classification, multimodal sensor fusion enables real-time oil health tracking, and digital twins provide predictive maintenance by forecasting lubricant degradation and optimizing drain intervals. AI-assisted blending and process control platforms extend these advantages into manufacturing, reducing waste and improving reproducibility. This article sheds light on recent progress in AI-driven formulation, monitoring, and maintenance, thus identifying major barriers to adoption such as fragmented datasets, limited model transferability, and low explainability. Moreover, it discusses how standardized data infrastructures, physics-informed learning, and secure federated approaches can advance the industry toward adaptive, sustainable lubricant development under the principles of Industry 5.0. Full article

The field of developing effective catalysts for heterogeneous catalysis has recently focused on controlling the structures of catalysts themselves to optimise the density and energy of crystal lattice defects. This can significantly influence catalytic activity in terms of both reaction rates and reaction mechanisms, and thus the selective production of desired substances as well. In many cases, these crystal lattice defects manifest themselves as so-called electron traps (ETs) and thus significantly influence charge transfer between the catalyst and reactants. ETs provide the missing electronic link between atomic-scale defects and macroscopic performance in heterogeneous catalysis. Therefore, the importance of ETs for catalysis is particularly evident in areas where charge transfer plays a fundamental role in the reaction mechanism, such as photocatalysis and electrocatalysis. In the field of thermally initiated reactions, the importance of ETs in heterogeneous catalysis has not yet been fully appreciated. However, several studies have already addressed the importance of ETs for this type of reaction. This review consolidates and extends the concept of ETs to purely thermal-initiated reactions, with a focus on CO₂ hydrogenation using typical transition metal catalysts. Firstly, in this review, ETs are defined as band gap states associated with internal and external defects, and their depth, density, spatial location, and dynamics are then coupled with key steps in thermocatalytic cycles, including charge storage/release, reactant activation, intermediate stabilisation, and redox turnover. Secondly, electron trap detection is reviewed based on advanced spectroscopic techniques, including reversed double-beam photoacoustic spectroscopy (RDB-PAS), thermally stimulated current (TSC), deep-level transient spectroscopy (DLTS), thermoluminescence (TL), electron paramagnetic resonance (EPR), and photoluminescence (PL), highlighting how each method describes trap energetics and populations under realistic operating conditions. Finally, case studies on the application of metal oxides and supported metals are discussed, as these are typical catalysts for the reaction mentioned above. This review highlights how oxygen vacancies (OVs), polarons, and metal–support interfacial sites act as robust electron reservoirs, lowering the barriers for CO₂ activation and hydrogenation. By reframing thermocatalysts through the lens of ET chemistry, this review identifies ETs as actionable targets for the rational design of next-generation materials for CO₂ hydrogenation and related high-temperature transformations. Full article

This manuscript provides a comprehensive review of the enzymatic hydrolysis of lignocellulosic biomass, emphasizing how chemical composition, structural features, inhibitory compounds, and process configurations collectively influence the conversion of structural polysaccharides into fermentable sugars. Variability among herbaceous, woody, and residual biomasses results in differences in cellulose, hemicellulose, lignin content, and crystallinity, which strongly affect enzyme accessibility. The review discusses key inhibitory mechanisms, including nonproductive cellulase adsorption onto lignin, interference from phenolic derivatives and pretreatment by-products, and inhibition caused by accumulating mono- and oligosaccharides. Process configurations such as SHF, SSF, PSSF, and consolidated bioprocessing are compared, with SSF often achieving superior performance by mitigating end-product inhibition. The manuscript also highlights the growing relevance of computational modeling and simulation tools, which support kinetic prediction, the evaluation of transport limitations, and the optimization of operating conditions in high-solids systems. Additionally, recent advances in artificial intelligence are presented as powerful approaches for modeling nonlinear hydrolysis behavior, estimating kinetic parameters, identifying rate-limiting steps, and improving predictive accuracy in complex bioprocesses. Overall, the integration of experimental insights with advanced modeling, simulation, and AI-based strategies is essential for overcoming current limitations and enhancing the technical feasibility and industrial competitiveness of lignocellulosic bioconversion. Full article

The H2IOSC project aims to establish a federated cluster of European distributed research infrastructures involved in the humanities and cultural heritage sectors, with operating nodes across Italy. Through four key RIs—DARIAH-IT, CLARIN, OPERAS, and E-RIHS—the project promotes collaboration among researchers with interdisciplinary expertise. Within this framework, DIGILAB functions as the digital access platform for the Italian node of E-RIHS. Conceived as a socio-technical infrastructure for the Heritage Science community, DIGILAB is designed to manage heterogeneous data and metadata through advanced knowledge graph representations. The platform adheres to the FAIR principles and supports the complete data lifecycle, enabling the development and maintenance of Heritage Digital Twins. DIGILAB integrates diverse categories of information related to cultural sites and objects, encompassing historical and artistic datasets, diagnostic analyses, 3D models, and real-time monitoring data. This monitoring capability is achieved through the deployment of cutting-edge Internet of Things (IoT) technologies and large-scale Wireless Sensor Networks (WSNs). As part of DIGILAB, we developed SENNSE (v1.0), a fully open hardware/software platform dedicated to environmental and structural monitoring. SENNSE allows the remote, real-time observation and control of cultural heritage sites (collecting microclimatic parameters such as temperature, humidity, noise levels) and of cultural objects (collecting object-specific data including vibrations, light intensity, and ultraviolet radiation). The visualization and analytical tools integrated within SENNSE transform these datasets into actionable insights, thereby supporting advanced research and conservation strategies within the Cultural Heritage domain. In the following sections, we provide a detailed description of the SENNSE platform, outlining its hardware components and software modules, and discussing its benefits. Furthermore, we illustrate its application through two representative use cases: one conducted in a controlled laboratory environment and another implemented in a real-world heritage context, exemplified by the “Biblioteca Bernardini” in Lecce, Italy. Full article

The demand for rapid and highly sensitive sensing technologies is increasing across diverse fields, including precise disease diagnosis, early-stage screening, and real-time environmental monitoring. Field-effect transistor (FET)-based sensing platforms have shown tremendous potential for detecting target molecules at extremely low concentrations, owing to their ultrahigh sensitivity, label-free and amplification-free operation, and rapid response. In recent years, the rapid advancement of nucleic acid probe design and interfacial engineering has markedly accelerated the development of FET sensors, leading to the emergence of nucleic acid-based FET (NA-FET) biosensors. Beyond their fundamental role in nucleic acid detection, the integration of nucleic acid aptamers and framework nucleic acids has greatly expanded NA-FET biosensors’ applicability to a wide range of analytes and multiplexed detection. At the same time, advances in semiconductor materials have endowed the NA-FET biosensor with highly efficient signal transduction and diverse device architectures, enabling successful proof-of-concept demonstrations for various clinically and environmentally relevant molecular biomarkers. Furthermore, the integration into portable, wearable, and implantable devices has laid a solid foundation for their future development into real-world applications. This review summarizes recent cutting-edge progress in NA-FET biosensors, highlights key design strategies and performance improvements, and discusses current challenges, future development directions, and their prospects for practical applications. Full article

The agri-food sector is a major contributor to global greenhouse gas emissions while facing increasing demand for food production driven by population growth. Transitioning towards sustainable and low-carbon agricultural systems is therefore critical. Green hydrogen, produced from renewable energy sources, holds significant promise as a clean energy carrier and chemical feedstock to decarbonize multiple stages of the agri-food supply chain. This systematic review is based on a structured analysis of peer-reviewed literature retrieved from Web of Science, Scopus, and Google Scholar, covering over 120 academic publications published between 2010 and 2025. This review provides a comprehensive overview of hydrogen’s current and prospective applications across agriculture and the food industry, highlighting opportunities to reduce fossil fuel dependence and greenhouse gas emissions. In agriculture, hydrogen-powered machinery, hydrogen-rich water treatments for crop enhancement, and the use of green hydrogen for sustainable fertilizer production are explored. Innovative waste-to-hydrogen strategies contribute to circular resource utilization within farming systems. In the food industry, hydrogen supports fat hydrogenation and modified atmosphere packaging to extend product shelf life and serves as a sustainable energy source for processing operations. The analysis indicates that near-term opportunities for green hydrogen deployment are concentrated in fertilizer production, food processing, and controlled-environment agriculture, while broader adoption in agricultural machinery remains constrained by cost, storage, and infrastructure limitations. Challenges such as scalability, economic viability, and infrastructure development are also discussed. Future research should prioritize field-scale demonstrations, technology-specific life-cycle and techno-economic assessments, and policy frameworks adapted to decentralized and rural agri-food contexts. The integration of hydrogen technologies offers a promising pathway to achieve carbon-neutral, resilient, and efficient agri-food systems that align with global sustainability goals and climate commitments. Full article

The main purpose of the present paper is to investigate biharmonic Legendre curves on hypersurfaces in four-dimensional complex space forms. We examine the necessary conditions for the existence of such curves on totally $\eta$-umbilical, ruled, and Hopf hypersurfaces for which the shape operator $A_N$ satisfies a symmetry property. The obtained results are discussed in the complex projective space $C{P}^2$. For any real hypersurfaces $(M,g)$ of $\tilde{M}\left(c\right)$, we have the relations ${\nabla }_X\xi =\varphi A_{N}X$ and $\left({\nabla }_X\varphi \right)Y=\eta \left(Y\right)A_{N}X-g(A_{N}X,Y)\xi$ for any $X,Y\in \mathrm{\Gamma }\left(TM\right)$. Using these equations, we provide the main results and theorems for biharmonic curves on the mentioned hypersurfaces. Full article

The smart grid concept is based on the full integration of different types of energy sources and intelligent devices. Due to the short- and long-term volatility of these sources, new flexibility measures are necessary to ensure the smart grid operates stably and reliably. One option is to convert renewable energy into hydrogen, especially during periods of generation overcapacity, in order that the hydrogen that is produced can be stored effectively and used “just in time” to stabilize the power system by undergoing a reverse conversion process in gas turbines or fuel cells which then supply power to the network. On the other hand, in order to achieve a sustainable general energy system (GES), it is necessary to replace other forms of fossil energy use, such as that used for heating and other industrial processes. Research indicates that a comprehensive hydrogen supply infrastructure is required. This infrastructure would include electrolyzers, conversion stations, pipelines, storage facilities, and hydrogen gas turbines and/or fuel cell power stations. Some studies in Germany suggest that the existing gas infrastructure could be used for this purpose. Further, nuclear and coal power plants are not considered reserve power plants (as in the German case), and an additional 20–30 GW of generation capacity in H₂-operated gas turbines and strong H₂ transportation infrastructure will be required over the next 10 years. The novelty of the approach presented in this article lies in the development of a unified modeling framework that enables the simultaneous and coherent representation of both economic and technical aspects of hydrogen production systems which will be used for planning and pre-decision making. From the technical perspective, the model, based on the black box approach, captures the key operational characteristics of hydrogen production, including energy consumption, system efficiency, and operational constraints. In parallel, the economic layer incorporates capital expenditures (CAPEX), operational expenditures (OPEX), and cost-related performance indicators, allowing for a direct linkage between technical operation and economic outcomes. This paper describes the systematic transformation from today’s power system to one that includes a hydrogen economy, with a particular focus on practical experiences and developments, especially in the German energy system. It discusses the components of this new system in depth, focusing on current challenges and applications. Some scaled current applications demonstrate the state of the art in this area, including not only technical requirements (reliability, risks) and possibilities, but also economic aspects (cost, business models, impact factors). Full article

In this work, we construct a new class of Appell-type polynomials generated through extended truncated and truncated exponential kernels, and we analyze their core algebraic and operational features. In particular, we establish a suitable recurrence scheme and obtain the associated multiplicative and differential operators. By confirming the quasi-monomial structure, we further deduce the governing differential equation for the proposed family. In addition, we present both a series expansion and a determinant formulation, providing complementary representations that are useful for symbolic manipulation and computation. As special cases, we introduce and study subfamilies arising from this setting, namely, extended truncated exponential versions of the Bernoulli, Euler, and Genocchi polynomials, and discuss their structural identities and operational behavior. Overall, these developments broaden the theory of special polynomials and furnish tools relevant to problems in mathematical physics and differential equations. Full article

Cybersecurity teams rely on signature-based scanners such as Loki, a command-line tool for scanning malware, to identify Indicators of Compromise (IOCs), malicious artifacts, and YARA-rule matches. However, the raw Loki log output delivered as CSV or plaintext is challenging to interpret without additional visualization and correlation tools. Therefore, this research discusses the creation of a web-based dashboard that displays results from the Loki scanner. The project focuses on processing and displaying information collected from Loki’s scans, which are available in log files or CSV format. DIGITRACKER was developed as a proof-of-concept (PoC) to process this data and present it in a user-friendly, visually appealing way, enabling system administrators and cybersecurity teams to monitor potential threats and vulnerabilities effectively. By leveraging modern web technologies and dynamic data visualization, the tool enhances the user experience, transforming raw scan results into a well-organized, interactive dashboard. This approach simplifies the often-complicated task of manual log analysis, making it easier to interpret output data and to support low-budget or resource-constrained cybersecurity teams by transforming raw logs into actionable insights. The project demonstrates the dashboard’s effectiveness in identifying and addressing threats, providing valuable tools for cybersecurity system administrators. Moreover, our evaluation shows that DIGITRACKER can process scan logs containing hundreds of IOC alerts within seconds and supports multiple concurrent users with minimal latency overhead. In test scenarios, the integrated Loki scans were achieved, and the end-to-end pipeline from the end of the scan to the initiation of dashboard visualization incurred an average latency of under 20 s. These results demonstrate improved threat visibility, support structured triage workflows, and enhance analysts’ task management. Overall, the system provides a practical, extensible PoC that bridges the gap between command-line scanners and operational security dashboards, with new scan results displayed on the dashboard faster than manual log analysis. By streamlining analysis and enabling near-real-time monitoring, the PoC tool DIGITRACKER empowers cyber defense initiatives and enhances overall system security. Full article

Localization and mapping remain critical challenges for Unmanned Ground Vehicles (UGVs) operating in unstructured natural environments, such as forests and agricultural fields. While Visual SLAM (VSLAM) and Visual–Inertial SLAM (VI-SLAM) have matured significantly in structured and urban scenarios, their extension to outdoor natural domains introduces severe challenges, including dynamic vegetation, illumination variations, a lack of distinctive features, and degraded GNSS availability. Recent advances in Deep Learning have brought promising developments to VSLAM- and VI-SLAM-based pipelines, ranging from learned feature extraction and matching to self-supervised monocular depth prediction and differentiable end-to-end SLAM frameworks. Furthermore, emerging methods for adaptive sensor fusion, leveraging attention mechanisms and reinforcement learning, open new opportunities to improve robustness by dynamically weighting the contributions of camera and IMU measurements. This review provides a comprehensive overview of Visual and Visual–Inertial SLAM for UGVs in unstructured environments, highlighting the challenges posed by natural contexts and the limitations of current pipelines. Classic VI-SLAM frameworks and recent Deep-Learning-based approaches were systematically reviewed. Special attention is given to field robotics applications in agriculture and forestry, where low-cost sensors and robustness against environmental variability are essential. Finally, open research directions are discussed, including self-supervised representation learning, adaptive sensor confidence models, and scalable low-cost alternatives. By identifying key gaps and opportunities, this work aims to guide future research toward resilient, adaptive, and economically viable VSLAM and VI-SLAM pipelines, tailored for UGV navigation in unstructured natural environments. Full article

Seafood processing environments represent some of the most demanding hygienic settings in the global food sector. High humidity, variable temperatures, and heavy organic residues promote the persistence of Listeria monocytogenes, Vibrio spp., and Salmonella spp., making sanitation both critical and inherently complex. This review synthesizes recent advances in hygienic design, sanitation technologies, and workforce safety as interconnected elements of a single “hygiene continuum.” Building upon Codex, FDA, and European hygiene frameworks (2020–2024), the review examines how engineering design, Sanitation Standard Operating Procedures (SSOPs) and Good Manufacturing Practices (GMPs) systems, and occupational hygiene jointly determine microbial control, sustainability, and workforce well-being. Particular focus is given to biofilm dynamics, emerging disinfection technologies, and automation through cleaning-in-place (CIP) and cleaning-out-of-place (COP) systems. Recent trends—including digital monitoring, eco-efficient cleaning, and human-centered facility design—are discussed as drivers of next-generation hygiene management. Collectively, these insights demonstrate that hygienic performance in seafood processing is not a fixed endpoint but a living system linking design, management, and human behavior toward safe, sustainable, and resilient seafood production. Full article

Ceramic Matrix Composites (CMCs) have emerged as a structural material alternative to nickel superalloys for high-pressure turbines (HPT) components operating at high temperature, like shrouds. Despite the outstanding thermal stability of the CMCs, limited cooling is still necessary due to the extreme thermal operating conditions necessary to maximize engine performance and minimize fuel consumption. The design of CMC components, indeed, must consider a maximum service temperature that should not be exceeded to avoid damage and accelerated oxidation. The cooling, on the other hand, may induce the formation of thermal gradients and thermal stresses. In this work, different design options for the cooling system are investigated to minimize the thermal stresses of an HPT shroud-like geometry subjected to maximum temperature constraints on the material. Cooling is obtained via colder air jet streams (air taken from the compressor), whose impact position (the surface where the cold air impacts the component) has a different effect on the temperature field and on the induced stress field. Besides stress evaluation with different cooling systems, an ONERA damage model is investigated at a key location to potentially take into account stress components acting simultaneously and potential stiffness degradation of the CMC. Finally, the design evaluation of potential discrete crack propagation is discussed. A standard cohesive elements approach has been compared with a brittle element death approach. The results showed that the cohesive element approach resulted in shorter crack propagation, underestimating the actual crack behavior due to the embedded stiffness degradation method, while the element death returned encouraging results as a quicker, less complex, but still accurate design evaluation. Full article